کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
7352883 1477050 2018 23 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Smooth calibration, leaky forecasts, finite recall, and Nash dynamics
ترجمه فارسی عنوان
کالیبراسیون صاف، پیش بینی های نشت، فراخوانی محدود، و دینامیس نش
موضوعات مرتبط
علوم انسانی و اجتماعی اقتصاد، اقتصادسنجی و امور مالی اقتصاد و اقتصادسنجی
چکیده انگلیسی
We propose to smooth out the calibration score, which measures how good a forecaster is, by combining nearby forecasts. While regular calibration can be guaranteed only by randomized forecasting procedures, we show that smooth calibration can be guaranteed by deterministic procedures. As a consequence, it does not matter if the forecasts are leaked, i.e., made known in advance: smooth calibration can nevertheless be guaranteed (while regular calibration cannot). Moreover, our procedure has finite recall, is stationary, and all forecasts lie on a finite grid. To construct the procedure, we deal also with the related setups of online linear regression and weak calibration. Finally, we show that smooth calibration yields uncoupled finite-memory dynamics in n-person games-“smooth calibrated learning”-in which the players play approximate Nash equilibria in almost all periods (by contrast, calibrated learning, which uses regular calibration, yields only that the time averages of play are approximate correlated equilibria).
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Games and Economic Behavior - Volume 109, May 2018, Pages 271-293
نویسندگان
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